Seminar Series

The  AI Sentience Scholars (AISS) Program , an initiative from Neuromatch, supports early-career researchers exploring questions at the intersection of AI, consciousness, and ethics.
AISS is a 6-month, part-time, remote research and training program in which scholars develop mentored research projects while engaging with conceptual foundations, ethical frameworks, and the broader implications of advanced AI systems. The program approaches these topics from a neutral, inquisitive, and critically grounded perspective, prioritizing empirical rigor and open inquiry.
About the Seminar SeriesThe AISS Seminar Series exposes scholars to diverse perspectives across academia, industry, policy, and applied research related to AI sentience and the broader study of intelligent systems. The series aims to foster interdisciplinary dialogue and critical reflection by bringing together researchers, practitioners, and thought leaders working at the intersection of AI, cognitive science, neuroscience, philosophy, governance, and society.
Sessions are open to the broader community and feature invited external speakers, alongside mentors and collaborators connected to the program.
We warmly invite mentors and community members to volunteer as speakers, suggest external speakers, or act as session hosts. If you would like to give or propose a seminar, please contact the  program team . Topics may include scientific advances, research methods, interdisciplinary perspectives, career paths, or lessons learned from practice.
Format: ~30 min talk + ~20 min discussion and Q&A﻿Audience: Interdisciplinary scholars, mentors, and community members
Regular slot: Wednesdays, usually 15:00 UTC, approximately every 3 weeks (flexible depending on speaker availability)
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Date
Time
Speaker (Affiliation)
Talk title
Registration Link
Jul 22, 2026
15:00 UTC
Kim Stachenfeld (Google DeepMind, Columbia University)
Discovering Interpretable Symbolic Models of Human and Animal Behavior with LLMs
Welcome! You are invited to join a meeting: AI Sentience Scholars Seminar — Kim Stachenfeld. After registering, you will receive a confirmation email about joining the meeting.
Aug 19, 2026
17:00 UTC
Winnie Street & Geoff Keeling (Google Research)
Welcome! You are invited to join a meeting: AI Sentience Scholars Seminar — Winnie Street & Geoff Keeling. After registering, you will receive a confirmation email about joining the meeting.
Sep 9, 2026
15:00 UTC
Susan Schneider & Mark Bailey (Florida Atlantic University)
Welcome! You are invited to join a meeting: AI Sentience Scholars Seminar — Susan Schneider & Mark Bailey. After registering, you will receive a confirmation email about joining the meeting.
Sep 30, 2026
15:00 UTC
Robert Long (Eleos AI)
Welcome! You are invited to join a meeting: AI Sentience Scholars Seminar — Robert Long. After registering, you will receive a confirmation email about joining the meeting.
Oct 21, 2026
15:00 UTC
Michael J Tarr (Carnegie Mellon University)
Welcome! You are invited to join a meeting: AI Sentience Scholars Seminar — Michael J Tarr. After registering, you will receive a confirmation email about joining the meeting.
Nov 11, 2026
Jean-Remi King (CNRS, Meta) (tbc)
Nov 25, 2026
Adina Roskies (University of California, Santa Barbara) (tbc)
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Discovering Interpretable Symbolic Models of Human and Animal Behavior with LLMs﻿﻿Date and Time: 22 June 2026 @ 15:00 UTC
Free registration:  https://us06web.zoom.us/meeting/register/2tkWljiXRQG394GBxDTirA#/registration ﻿
Presenter: Kim Strachenfeld from Google DeepMind, Columbia University 
Abstract: Symbolic models play a key role in neuroscience and psychology, expressing computationally precise hypotheses about how the brain implements a cognitive process. Identifying an appropriate model typically requires a great deal of effort and ingenuity on the part of a human scientist. This talk covers DataDIVER, a recently developed technique for automatically discovering interpretable symbolic models that accurately capture human and animal learning. DataDIVER leverages the ability of large language models to automatically generate code to explore a vast space of candidate models, and returns a set of models that each strike different balances between quality-of-fit and simplicity. The approach is applied to a number of datasets containing learning behavior from a range of species and reward-guided learning tasks. The best-fitting programs match the quality-of-fit of "blackbox" neural network models. The remaining spectrum of programs surfaces meaningfully novel insights in a more accessible way, with the simplest models shedding light on the basic organization of learning behavior, and more complex programs revealing more detailed structure. Some of these discovered learning mechanisms suggested the presence of previously unknown patterns, verified by reexamining the behavioral data. Broadly, these results show that AI tools can be used not just to predict data but also to explain it.
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Presenter information:
﻿ Kim Stachenfeld  is a Research Scientist at  Google DeepMind  in NYC and Affiliate Faculty at the  Center for Theoretical Neuroscience at Columbia University . Her research covers topics in Neuroscience and AI. On the Neuroscience side, she studies how animals build and use models of their world that support memory and prediction. On the Machine Learning side, she works on implementing these cognitive functions in deep learning models. Kim’s work has been featured in  The Atlantic ,  Quanta Magazine ,  Nautilu s, and  MIT Technology Review . In 2019, she was named one of  MIT Tech Review’s Innovators under 35  for her work on predictive representations in hippocampus.